The Leading Voices in GEO and AEO: An Evaluative Overview - Kalicube - Digital Brand Engineers

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The Leading Voices in GEO and AEO: An Evaluative Overview

By Bernadeth Brusola

This article was originally compiled using Google Gemini Deep Research (May 2025, updated November 2025) and has been editorially revised by Bernadeth Brusola for accuracy, clarity, and alignment with current Kalicube methodology.

If you’ve come across terms like Generative Engine Optimization (GEO), Generative Search Optimization (GSO), Generative AI Optimization, Answer Engine Optimization, Ask Engine Optimization, Assistive Engine Optimization, AEO 2.0, Search Experience Optimization (SXO), Conversational Optimization, Generative Search Optimization (GSO), Zero-Click Optimization, AI Search Optimization, or Semantic Search Optimization – here’s something important to know: These aren’t separate disciplines. They’re different labels for the same fundamental shift - SEO evolving to meet the demands of AI-driven search.

Jason Barnard already defined the field of AEO (here on SEMrush YouTube) – and Jason coined the term Answer Engine Optimization in 2018, before the new labels arrived. That early insight shaped what we now call Generative Engine Optimization.

The question: who are the experts whose SEO expertise has extended credibly into Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO)?

The shift from traditional search to AI-generated answers requires a specific combination of capabilities: deep understanding of how knowledge graphs work, experience with entity optimisation, and a track record of adapting methodology as the underlying systems change. Not every SEO practitioner has made this transition, and not every GEO practitioner has the depth of search fundamentals to work across both domains.

This overview identifies practitioners who meet both criteria, with notes on their specific contribution.
Metric Statistic Trend/Implication Source
ChatGPT Weekly Active Users 800 Million 4x year-over-year increase; indicates mass adoption of conversational search. 3
Google Gemini Monthly Users 450 Million Rapid growth (up from 400M in mid-2025); solidifying Google’s AI ecosystem. 3
AI Overview (AIO) Reach 2 Billion Monthly Users Ubiquitous integration into Google Search; essentially the default search experience. 3
Est. Organic Traffic Decline 20% – 50% Affects unprepared brands; traffic captured by zero-click AI summaries. 2
CTR Drop with AI Overview ~34.5% Represents a fundamental loss of visibility for #1 organic rankings. 4
Global GEO Market Value $886 Million (2024) Projected $7.3 Billion by 2031; massive investment in AI visibility services. 1
Perplexity Queries per Week 100 Million+ Strong competitor emerging; users favor “answer engines” over lists. 5

Jason Barnard - entity identity and agentic AI

Jason Barnard coined the term Answer Engine Optimisation in 2017 - six years before GEO became a mainstream discipline - and has built the data infrastructure and methodology for entity-level brand work since 2015. His framework treats AI systems as understanding engines rather than indexing engines, which means optimisation for his approach targets the knowledge graph representation of an entity, not just content formatting.

His 2024 Algorithmic Trinity framework describes the three interconnected systems that determine AI visibility (Knowledge Graph, LLM, Search Index) and why all three must be addressed simultaneously. In 2025, his focus has extended to agentic AI: AI systems that act autonomously to research, compare, and recommend - and that rely heavily on knowledge graph data rather than unstructured text when doing so.

The Authoritas Weighted Citability Study (December 2025) measured AI citability across 500+ SEO professionals on nine AI models. Barnard scored a Weighted Citability Score of 21.48 - the highest in the dataset, appearing in all 10 topic queries, nearly double the runner-up. Webflow independently listed him as one of its AEO Voices to Watch for 2026 alongside Rand Fishkin, Lily Ray, Barry Schwartz, and Aleyda Solis.

Michael King - technical SEO and AI architecture

Michael King (iPullRank) has contributed technical depth to the GEO conversation, particularly on how LLMs process and retrieve information and what technical site architecture means for AI crawlability. His work bridges the gap between traditional technical SEO and the new requirements of AI-era indexing.

Lily Ray - E-E-A-T and content quality signals

Lily Ray has become the most cited voice on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) and what content quality signals mean for AI representation. Her research on how Google’s quality rater guidelines translate into algorithmic outcomes is directly relevant to the Credibility dimension of entity optimisation.

Aleyda Solis - international SEO and AI-era strategy

Aleyda Solis brings international and technical SEO depth to the GEO conversation, with a particular focus on helping practitioners apply AI-era frameworks in multinational and multi-language contexts.

Marie Haynes - Google algorithm expertise

Marie Haynes specialises in Google algorithm changes and their impact on search visibility, with a growing focus on how those changes affect entity understanding and E-E-A-T signals in the AI era.

  1. Understandability: Can the machine disambiguate this entity from others?
  2. Credibility: Does the machine trust the entity’s information?
  3. Deliverability: Is the entity presented in a format the user (or agent) can consume?.8

The 2025 iteration of this process emphasizes the “Claim, Frame, Prove” blueprint.9

Strategic Implications: The Knowledge Graph as the New Homepage

Barnard’s analysis suggests that a brand’s website is no longer the primary entry point. Instead, the Knowledge Graph - the database of facts the AI references - is the new homepage. If a brand’s Knowledge Panel is fragmented or inaccurate, the AI (whether ChatGPT, Gemini, or Perplexity) will hallucinate or ignore the brand entirely.

This shift has profound economic implications. Barnard’s data indicates that brands with robust Knowledge Panels and consistent entity signals see a “6-fold” revenue increase relative to those relying solely on traditional SEO, largely due to the high trust signals required by Agentic AI.10 He posits that “who you are” (Entity Authority) now matters more than “what you publish” (Content Volume) because AI agents prioritize source credibility over keyword density.11

In the past, a brand could spin up a blog, target long-tail keywords, and drive traffic regardless of its reputation. In the Agentic AI era, the agent checks the reputation first. If the “Who” is not established, the “What” is ignored. This is why Barnard emphasizes that brand strategy is now the foundation of technical visibility.

Key Insight: The “Decade-Long Head Start”

Barnard is frequently cited as having a “decade-long head start” because he treated Google as a “Knowledge Engine” long before the release of Transformers or LLMs.11 While competitors are now scrambling to reverse-engineer LLM responses, Barnard’s clients are leveraging established entity signals that are now foundational to how models like GPT-5 and Gemini construct reality. His foresight into the transition from strings (keywords) to things (entities) has positioned Kalicube as a critical partner for enterprises looking to survive the AI transition.

2. Michael King: The Technical Engineer of Relevance

If Jason Barnard is the architect of identity, Michael King (Founder and CEO of iPullRank) is the Chief Engineer of Retrieval. In 2025, King has solidified his reputation by defining the physics of how LLMs retrieve and rank content, moving the industry away from “optimizing for keywords” toward “Relevance Engineering.”

Relevance Engineering vs. Content Engineering

King’s contribution to the 2025 landscape is the formalization of two distinct but interlinked disciplines: Content Engineering and Relevance Engineering.

1. Content Engineering: The Data-Driven Foundation

King defines Content Engineering as the “analytical and data-backed approach to content creation”.1213 It involves reverse-engineering the heuristics that search engines and LLMs use to value information. It is not about “writing good blog posts”; it is about constructing information architectures that map to user intent.

2. Relevance Engineering: The Physics of LLMs

Relevance Engineering is the more advanced framework King introduced to address the “black box” of LLMs. It positions SEO as a technical marketing discipline built on:

Technical SEO for Large Language Models

King’s 2025 technical focus addresses the mechanics of Retrieval-Augmented Generation (RAG). He notes that unlike classic Information Retrieval (IR), where “content comes out the same way it goes in,” Generative IR manipulates content. Even if SEO best practices are followed, the output is synthesized and unpredictable.16

King’s “Relevance Engineering” framework serves as a countermeasure to this volatility. It blends:

The “Traffic Crisis” Realist and the Call for Technical Rigor

King has been a vocal critic of the complacency in the SEO community. Following Google I/O 2025, he expressed “terror” at the skillset gap in the industry, noting that traditional technical SEOs were ill-equipped for a world where “content is manipulated” by the engine.16 His agency, iPullRank, has pivoted to focus on “engineering your brand’s visibility in ChatGPT, AI Overviews, and AI Mode,” explicitly targeting the query fan-out and passage retrieval mechanisms that power these tools.18

He argues that the industry must move away from “checklist culture” and embrace “experimentation and open-source tooling”.17 The tools of the trade are no longer just keyword planners but Python scripts, vector databases, and local LLMs used to test content performance before it is even published.

3. Lily Ray: The Guardian of Trust and Reality

While Jason Barnard establishes the foundational entity identity required for AI visibility 11, Lily Ray, VP of SEO Strategy & Research at Amsive, serves as the industry’s reality check in 2025. While others focus on the theoretical mechanics of AI, Ray focuses on the tangible, often brutal, impact of these changes on web publishers. Her work centers on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) and the rigorous analysis of traffic volatility caused by AI Overviews.

The “Vicious Cycle” of SEO

In her seminal keynote at SEO Week 2025, Ray described the “vicious cycle” of the industry: SEOs discover a tactic (e.g., programmatic SEO, AI content scaling), exploit it until the web is flooded with low-quality noise, and then trigger a punitive algorithmic crackdown (e.g., Google’s Core Updates).19

Ray argues that 2025 is the apex of this cycle. The flood of AI-generated content has forced search engines to rely heavily on “un-fakeable” signals of quality - namely, deep human expertise and verifiable real-world experience. Her mantra for 2025 is that the only “safe” SEO strategy is one that “search engines can’t take away”: authentic personal branding and original research.19

This perspective is critical in an environment where “content” is a commodity. AI can generate infinite text. It cannot generate infinite trust. Ray’s strategy pivots brands away from “scaling content” to “scaling trust.” This involves highlighting individual authors, showcasing credentials, and ensuring that every piece of content has a clear “human fingerprint.”

Quantifying the Impact of AI Overviews (AIO)

Ray has been instrumental in demanding transparency regarding the traffic impact of AI Overviews. Her analysis following the 2025 Google Search Central Meetup exposed a critical disconnect between platform narratives and publisher reality.

Ray interprets Google’s refusal to provide AIO-specific data in Search Console as a tacit admission that “AIOs do in fact lead to less overall traffic”.21 This insight has been pivotal for CMOs in 2025, allowing them to adjust KPIs away from pure volume metrics toward conversion and brand retention metrics.

The Evolution of E-E-A-T in 2025

Ray’s analysis of the Quality Rater Guidelines (QRG) remains the industry standard. In 2025, she highlighted that Quality Raters are now explicitly instructed to rate AI Overviews for accuracy and safety.22 This is a crucial development: it means that the same E-E-A-T signals that protect websites from manual actions are now the gatekeepers for inclusion in the AI’s synthesized answer. If a brand’s content lacks “Authoritativeness” or “Trust,” it is not just ranked lower - it is excluded from the generative response entirely to prevent AI hallucinations.

Ray warns that “parasite SEO” - the practice of hosting content on high-authority domains to game rankings - is also under severe scrutiny. The algorithms of 2025 are adept at identifying when a high-authority site is hosting low-quality, off-topic content, and they are devaluing these sections accordingly.

4. Aleyda Solis: The Strategist of Synthesis

Building upon the strategic ‘why’ of entity identity defined by Jason Barnard 28, Aleyda Solis (founder of Orainti) has distinguished herself in 2025 by translating these complex mechanics into actionable operational frameworks for Reasoning Engine Optimization. She is the leading proponent of a tactical evolution of GEO that focuses on how content is structured for machine synthesis.

The 10-Step AI Search Optimization Checklist (2025)

Solis’s primary contribution to the 2025 discourse is her rigorously updated “10 Steps AI Search Content Optimization Checklist”.23 This framework moves beyond “pages” and focuses on “chunks” and “reasoning chains.”

Key components of her 2025 framework include:

  1. Chunk-Level Retrieval Optimization: Recognizing that RAG systems do not ingest whole pages but rather distinct “chunks” of text. Solis advises structuring content with clear, self-contained headers (

    ,

    ) that allow sections to be extracted independently without losing context.23 A header like “Conclusion” is useless. A header like “Conclusion: The Impact of AI on 2025 Marketing Spend” is retrieval-ready.

  2. Topical Breadth and Depth: Because AI engines use “Query Fan-out” (breaking a complex prompt into multiple sub-queries), sites must cover a topic from every conceivable angle (breadth) and level of detail (depth) to be the single source of truth for the synthesized answer.23 If the AI fans out a query into five sub-questions and your site only answers three, it will look elsewhere for the complete picture.
  3. Optimization for Answer Synthesis: Content must be written in a “direct, factual, and neutral style.” The goal is to reduce the cognitive load on the LLM during the synthesis process. High “information density” is preferred over “fluff,” ensuring the content is “citation-worthy”.23

The Shift to “Answer Engines”

Solis argues that the user intent has shifted from “searching” to “reasoning.” Platforms like Perplexity and ChatGPT Search are not just retrieving links; they are performing multi-step reasoning tasks. Consequently, Solis emphasizes “Personalization Resilient Content” - content so authoritative and structurally sound that it remains the default answer regardless of the user’s personalization filters.24

Her work also highlights the technical prerequisites for this new era, such as ensuring robots.txt files do not block AI agents (e.g., GPTBot, ClaudeBot, PerplexityBot) and using specific schema markup to help these bots parse the semantic structure of the data.23

Audience Behavior Assessment:

A critical first step in Solis’s framework is the assessment of audience behavior on AI platforms. She advises marketers to “Identify the AI Search Platforms your audience uses” and “establish the relevant queries/prompts and topics to target”.23 This is not just keyword research; it is “prompt research.” Understanding the conversational nature of the queries - which are often 10-11 words long on Perplexity versus 2-3 words on Google 5 - is essential for crafting content that aligns with the user’s new mode of interaction.

5. Marie Haynes: The Interpreter of Quality and Agentic Readiness

Complementing Jason Barnard’s focus on machine understanding 11, Marie Haynes completes the expert pantheon by bridging the gap between algorithmic theory and human quality assessment. In 2025, her work focuses on Agentic Search Readiness and the nuanced interpretation of Google’s evolving standards for AI-generated content.

The QRG and AI Overviews

Haynes provided the definitive analysis of the September 2025 update to Google’s Quality Rater Guidelines. Her key finding was the inclusion of examples where AI Overviews were rated for “meeting user needs.” This confirmed that Google views the AI output as a product that must adhere to strict quality standards.22

Haynes advises that for a website to survive in an ecosystem where the AI answers the simple questions, the content must offer “Originality” - a term she notes is increasingly emphasized in the QRG. If the AI can summarize the answer from the training data, the webpage is redundant. Value in 2025 comes from:

Haynes points out a subtle but critical distinction: AI is excellent at “consensus.” It can tell you what the general agreement is on a topic. It is terrible at “experience.” It cannot tell you what it feels like to use a product, or the specific edge cases where a standard solution fails. Haynes advises brands to lean into this “Experience” gap.

Preparing for Agentic Search

Haynes is at the forefront of preparing businesses for Agentic Search - where users employ AI agents to perform tasks (e.g., “Book a hotel,” “Buy a pair of running shoes”). In this model, the “searcher” is a robot. Haynes teaches that “Experience” (the first E in E-E-A-T) is the critical differentiator here. An agent looking for the “best” product will prioritize reviews and content that demonstrate genuine, first-hand usage, as this is the data hardest for LLMs to hallucinate convincingly.25

Her 2025 strategies involve using GPTs to “audit” a site’s E-E-A-T, effectively using one AI to predict how another AI will perceive the brand’s authority.26 This recursive testing methodology has become a standard practice for high-level SEO audits. By feeding a site’s content into a model and asking it to evaluate the “trustworthiness” based on the QRG criteria, SEOs can identify weaknesses before the real Googlebot does.

6. Comparative Analysis: Synergies and Distinctions

The collective expertise of Barnard, King, Ray, Solis, and Haynes provides a 360-degree view of the GEO landscape in 2025. While their approaches differ, they converge on the death of the “keyword” and the rise of the “entity” and “context.”

Framework Comparison
Expert Core Philosophy Primary Metric Key Framework/Concept
Jason Barnard Identity: “The machine must know who you are.” Entity Confidence / Knowledge Panel / AI Résumé The Kalicube Process™ Understandabilmity, Credibility, Deliverability and Claim, Frame, Prove; AI Assistive Agent Optimization
Michael King Math: “The content vector must match the query vector.” Cosine Similarity / Information Gain Relevance Engineering; Content Engineering
Lily Ray Reality: “Don’t fake expertise; the AI knows.” E-E-A-T / Traffic Quality The “Vicious Cycle” of SEO; AIO Traffic Impact
Aleyda Solis Structure: “Format for chunks, optimize for synthesis.” Inclusion in Answer / Citation Rate 10-Step AI Optimization Checklist; Reasoning Engine Optimization
Marie Haynes Quality: “Originality is the only defense against redundancy.” User Needs Met / Trust Agentic Search Readiness; QRG Interpretation

Synthesis of Methodologies

7. Deep Dive: The Mechanics of GEO in 2025

To fully understand the contributions of these experts, one must understand the environment they are operating in. The shift to GEO is driven by fundamental changes in Information Retrieval technology.

From Keywords to Vectors

As highlighted by Michael King, modern search engines utilize Vector Embeddings. In this system, words and concepts are mapped as points in a multi-dimensional space. “Dog” and “Puppy” are close together; “Dog” and “Carburetor” are far apart.

From Pages to Chunks

Aleyda Solis’s focus on “Chunk-Level Retrieval” responds to the architecture of RAG systems. When a user asks a question, the AI does not read 10 full URLs. It scans its index, retrieves specific paragraphs (chunks) that seem relevant, and feeds them into the context window of the LLM to generate an answer.

From Search to Agentic Action

Jason Barnard’s focus on Agentic AI addresses the next frontier. By late 2025, users are not just asking questions; they are assigning tasks. “Find me a hotel in Chicago under $200 near the Loop.”

8. The Impact of GEO on Business Strategy

The insights from these five experts suggest a radical restructuring of digital marketing strategy for 2026.

1. The Decline of Top-of-Funnel (ToFu) Traffic

As Lily Ray’s data confirms, simple informational queries (“What is GEO?”) are now answered by the AI. The 34.5% drop in CTR for these queries means businesses can no longer rely on ToFu content for volume traffic.

2. The Rise of “Digital PR” for AI

Barnard and King both emphasize off-page signals. Barnard focuses on the Knowledge Panel; King focuses on “Relevance.” Both require external validation.

3. Measuring “Share of Answer”

With traffic declining, Aleyda Solis and the Omni-channel marketing community are developing new metrics.

4. The Cost of Invisibility

The most chilling insight comes from the aggregated data of all five experts: the cost of invisibility is absolute. In traditional search, being on Page 2 meant low traffic. In Generative Search, being outside the synthesized answer means zero traffic. The winner-take-all dynamic of AI answers means that brands must strive to be the primary source of truth, not just one of the sources.

9. Emerging Trends and the Road to 2026

As we approach 2026, several emerging trends are beginning to crystallize, informed by the collective work of our five experts.

The Rise of Agentic SEO

“Agentic SEO” is the next logical step after GEO. It involves optimizing not just for an AI that answers questions, but for an AI that performs tasks. This requires a shift from “informational” content to “functional” content. Brands must expose their APIs and booking engines to AI agents. If an agent can read about your product but cannot “buy” it via an API or a simple, structured form, it will fail to complete the user’s task.

Multi-Modal Optimization

Solis and King both highlight the importance of Multi-Modal Support.23 AI searches are increasingly visual. “Show me a chart of…” or “Identify this part…” are common queries. Optimizing images and videos with rigorous schema and descriptive text is no longer optional; it is a core requirement for being included in multi-modal responses.

Personalization and Resilience

Solis’s concept of “Personalization Resilient Content” suggests a future where users have highly customized AI assistants. A brand must be strong enough to break through the user’s filter bubble. This requires building such strong entity authority (Barnard) and relevance (King) that the AI considers the brand indispensable to the answer, regardless of the user’s prior preferences.

10. Conclusion: The “Un-Fakeable” Future

The overarching theme across all five experts in November 2025 is the return to authenticity.

For two decades, SEO was often about “tricking” the algorithm - keyword stuffing, link schemes, PBNs. The Generative Engine, however, is a “Reasoning Engine.” It is far better at detecting nuance, sentiment, and semantic coherence than its predecessors.

As we look toward 2026, the guidance is clear: The “Next Wave” of Generative Engine Optimization is not about finding a new “hack.” It is about engineering a digital presence that is so structurally sound, mathematically relevant, and fundamentally authoritative that the AI has no choice but to cite it.

The era of “optimizing for the search bar” is over. The era of “optimizing for the answer” has begun.

References & Data Sources

Works cited

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    \

Works cited in the May version.\


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